This repository serves as a curated reading list for instruction tuning in large language models, tracking the evolution of techniques from early works like Natural-Instruction and FLAN to more recent advancements. It is intended for researchers and practitioners in NLP and LLM development seeking to understand and implement methods for improving model generalization and multi-task learning through natural language instructions.

How It Works

The project compiles a chronological list of research papers that explore instruction tuning. This approach allows users to trace the development of the field, understand the foundational concepts, and identify key methodologies and datasets that have emerged. The papers cover various aspects, including cross-task generalization, zero-shot learning, prompt-based pre-training, and the impact of human feedback or self-generated instructions.

Quick Start & Requirements

This repository is a collection of research papers and does not require installation or execution. All papers are linked via provided URLs.

Highlighted Details

• Comprehensive list of seminal and recent papers on instruction tuning.
• Covers a wide range of techniques including prompt-based learning, multi-task fine-tuning, and self-instruct methods.
• Tracks the trend from ACL 2022 (Natural-Instruction) through ICLR 2022 (FLAN, T0) up to mid-2023.
• Includes papers focusing on specific domains like biomedical NLP, dialogue systems, and visual instruction tuning.

Maintenance & Community

The repository is maintained by SinclairCoder. There are no explicit mentions of community channels, active development, or a roadmap.

Licensing & Compatibility

The repository itself does not have a specified license. It is a collection of links to external research papers, each with its own licensing and terms of use.

Limitations & Caveats

This repository is a static list of papers and does not provide code, datasets, or implementations. It is purely an informational resource for understanding the research landscape of instruction tuning.